# Competency Frameworks

Structured definitions of the observable capabilities a role requires, used for assessment and development.

**Rests on:** Talent Assessment

**Leads to:** Learning Agility, Succession Planning

*Business, Career and Human Factors · Talent Management*


## Why learn this

This concept is the foundation for everything you will study next in talent management. Without a clear framework, you cannot assess someone fairly, you cannot target development where it matters, and you cannot prepare people for roles that do not yet exist. Master this and you unlock: Talent Assessment (how you actually measure competencies), Behaviourally Anchored Rating Scales (how you make those measurements consistent), Adaptive Expertise (the kind of expertise frameworks should aim to develop), and Succession Planning (how you use frameworks to decide who is ready for the next role). In an AI-shaped workplace, frameworks are also what let you decide which human capabilities to invest in — and which to hand over to machines.


## The intuition

Think of a job you know well — maybe a sales role, a software developer, or a nurse. If someone asked you, 'What does a good salesperson actually do?', you could list some behaviours: they listen more than they talk, they follow up within a day, they know when to push and when to back off. None of these are innate talents. They are things people learn to do through practice, feedback, and deliberate effort. A competency framework is simply that list, but written down in a structured, systematic way. It answers three questions: What observable things must someone in this role be able to do? How good is 'good enough' for each? And how do we measure whether someone has reached that level? The key word is 'observable' — a competency is not 'has a good attitude' (you cannot see that) but 'responds to customer complaints within one working day' (you can see that, and you can check it). A framework gives you a shared language: instead of 'he is not a team player' (vague, subjective), you can say 'he does not meet the competency of Collaboration at level 2, which is defined as actively seeking input from two or more colleagues before finalising a decision'. That specificity is what makes assessment fair and development targeted.


## An analogy

Think about a recipe for biryani. A great biryani chef does not rely on intuition alone — they follow a recipe. The recipe breaks the dish into components: the rice, the marination, the layering, the dum cooking. For each component, it specifies what 'done' looks like — the rice should be separate grains, not sticky; the chicken should be tender but not falling apart; the aroma should hit when you lift the lid. A competency framework is the recipe for a role. It lists the components of the role (say, 'negotiation', 'data analysis', 'stakeholder management'). For each, it describes levels of mastery — like 'novice', 'proficient', 'expert' — each level anchored to specific, observable behaviours. Just as a recipe lets a new cook replicate a complex dish, a framework lets a new manager assess and develop people consistently, even if they have never worked in that role themselves. The framework does not make someone a great chef, but it tells you what to practise and how to know you have improved. Now, where the analogy stops working: a recipe is static — once written, it stays the same. A competency framework is not static. Roles change as the business changes, and a framework that is not revisited yearly becomes a fossil. Also, cooking has a single correct output, but a role can be performed excellently in many different ways — the framework should describe outcomes and behaviours, not a single rigid method.


## Definition

A competency framework is a structured set of definitions of the observable capabilities a role requires, each capability described at multiple levels of proficiency, used as the basis for assessing performance and planning development.


## How it connects

You have not studied any other topics yet, so I will build this from scratch. However, this concept connects forward to several you will meet soon: Talent Assessment (how you measure competencies reliably), Behaviourally Anchored Rating Scales (the concrete anchors that make frameworks less subjective), Adaptive Expertise (the kind of expertise frameworks should aim to develop as a goal of development), and Succession Planning (using frameworks to identify who is ready for the next role). In the broader talent management picture, competencies are the measurable outputs of systematic development — as your library notes — which makes them the hinge between 'we hire smart people' and 'we deliberately grow our people'.


## The mathematics

This concept has no mathematical operations. It relies on classification and rating, not calculation. But the idea of 'level' is quantitative: you can rate a behaviour on a scale, say 1 to 5. This is ordinal arithmetic—we can compare orders, not quantities. The branch of mathematics is arithmetic and basic statistics (counting, averaging), but no formulas are needed to define or use a framework.

No formula beyond averaging: Overall score = (Σ scores for each competency) / number of competencies. Each score itself is an average of raters' scores. This is arithmetic only.


## A worked example

```
Consider a role 'Data Analyst'. The framework lists three competencies: Data Interpretation, SQL Proficiency, and Communication. Each is rated on a scale of 1–5.

Step 1: Rater 1 gives: Data Interp=4, SQL=3, Comm=5   # First rater's assessment
Step 2: Rater 2 gives: Data Interp=3, SQL=4, Comm=4   # Second rater's assessment
Step 3: Average Data Interp: (4+3)/2 = 3.5   # Combine ratings
Step 4: Average SQL: (3+4)/2 = 3.5, Avg Comm: (5+4)/2 = 4.5   # Averages for other competencies
Step 5: Overall competency score = (3.5+3.5+4.5)/3 = 3.83   # Average across competencies

Result: The analyst scores 3.83 out of 5, indicating a solid mid-to-high level, but with room for improvement in SQL and Data Interpretation.
```


## From scratch, in Python

You don't need code to use a competency framework, but you can write a simple program to store and average ratings. Here's plain Python:

```python
# A competency framework is just a list of competencies and a rating scale.

competencies = ['Data Interpretation', 'SQL Proficiency', 'Communication']

# Ratings from two raters, each is a list of three scores (one per competency)
rater1 = [4, 3, 5]
rater2 = [3, 4, 4]

# Combine ratings by averaging each competency
combined = []
for i in range(len(competencies)):
    avg = (rater1[i] + rater2[i]) / 2
    combined.append(avg)

# Show the combined scores
print('Competency      Avg Score')
for i, comp in enumerate(competencies):
    print(f'{comp:20} {combined[i]:.2f}')

# Overall score: average of all combined scores
overall = sum(combined) / len(combined)
print(f'\nOverall score: {overall:.2f}')
```

Output:
```
Competency      Avg Score
Data Interpretation   3.50
SQL Proficiency       3.50
Communication         4.50

Overall score: 3.83
```


## The same thing, with a framework

```python
If you were building an HR system, you might use a database (like SQL) to store frameworks and ratings, or a library like pandas for analysis. But the core logic is the same: storing lists of competencies and averaging numbers.
```


## What the code does

We define a list of competencies, then two lists of ratings. We loop over the indices (0,1,2) to pair ratings for each competency, average them, and store in a new list. Then we use `enumerate` to print each competency with its average, and `sum/len` to compute the overall average. This shows how a framework is just structured data.


## Common misconceptions

**Competencies are fixed personality traits** — A competency framework just measures what kind of person someone is — like 'creative' or 'organised'. If you are not creative by nature, you never will be.

Competencies are explicitly defined as developable behaviours. They can be improved with practice and feedback. Traits are relatively stable; competencies are developed — that is the entire point of a framework. If you measure a trait, you cannot plan development. In fact: Competencies are not personality traits. They are observable behaviours that people can learn. A framework is a tool for development, not a label.

**A framework is permanent once written** — Once a framework is written, it should stay the same — changing it would make assessments inconsistent.

Roles change as the business and technology change. An outdated framework measures the wrong things, and assessment against it is worse than no framework. The framework must be revised, even at the cost of some short-term inconsistency. In fact: A framework is a living document. It should be reviewed at least annually and revised when roles shift.

**More levels mean more precision** — A framework with ten levels is more precise than one with three.

Too many levels are hard to distinguish in practice. Raters cannot reliably tell a 7 from an 8. Fewer, well-defined levels with concrete behavioural anchors are more reliable. In fact: The number of levels should match how well raters can actually distinguish them. Three to five levels with clear behavioural anchors often work best.


## In short

A competency framework is a structured list of the observable, developable behaviours a role requires, each defined at multiple levels. It is the basis for fair assessment and targeted development. Frameworks are not static — they must evolve as roles change. No mathematics and no code are needed, but the clarity of the definitions is a precision problem like code reviews — ambiguity breeds inconsistency.


## Where this is used

Competency frameworks are not a coding concept — they are a people-management tool. So where does the 'building AI systems' question fit? It fits in three ways. First, in the AI industry itself, companies like Google and Microsoft use competency frameworks to define what a 'Machine Learning Engineer' or 'AI Product Manager' must be able to do. When you hear that a company has a 'career ladder' that lists levels from L3 to L7, that ladder is a competency framework. Developers are assessed against it for promotion, and the framework shapes what training they take. Second, when an AI system is built to assist with talent management — for example, an AI that screens résumés or suggests training courses — the AI is typically trained to match people against a competency framework. The framework is the specification of what the AI should look for. If the framework is vague ('good communication'), the AI will be vague; if it is precise ('gives a clear, structured update in under 5 minutes'), the AI can be trained to recognise that behaviour. Third, the rise of AI is itself changing competency frameworks. As routine tasks get automated, the competencies that matter are shifting towards adaptive expertise, learning agility, and judgement. A good framework is not static — it must be revised as roles change, exactly as your library notes. In practice, a developer building a talent management system will first need to ingest a competency framework — perhaps a JSON file listing competency names, descriptions, and levels. They will write code to store that framework, to attach assessment scores to it, and to generate reports. They will also build the logic that maps assessment scores to development recommendations. None of that requires mathematics, but it does require careful data modelling — which is a software engineering skill.


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Read this on the web: https://basavapurushottam.com/learn-ai/competency-frameworks
